Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	427	422
Missing cells (%)	8.0%	7.9%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 88 (19.7%) missing values	`Age` has 83 (18.6%) missing values	Missing
`Cabin` has 338 (75.8%) missing values	`Cabin` has 338 (75.8%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 311 (69.7%) zeros	`SibSp` has 308 (69.1%) zeros	Zeros
`Parch` has 342 (76.7%) zeros	`Parch` has 345 (77.4%) zeros	Zeros
`Fare` has 11 (2.5%) zeros	`Fare` has 9 (2.0%) zeros	Zeros
Alert not present in this dataset	`Sex` is highly overall correlated with `Survived`	High correlation
Alert not present in this dataset	`Survived` is highly overall correlated with `Sex`	High correlation

Reproduction

	Dataset A	Dataset B
Analysis started	2025-03-11 15:19:02.612077	2025-03-11 15:19:05.085419
Analysis finished	2025-03-11 15:19:05.082227	2025-03-11 15:19:07.626429
Duration	2.47 seconds	2.54 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	433	446.37444

	Dataset A	Dataset B
Minimum	1	3
Maximum	884	891
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	3
5-th percentile	52.5	41.25
Q1	216.25	232.75
median	427	439.5
Q3	647.25	671.75
95-th percentile	832.75	851.5
Maximum	884	891
Range	883	888
Interquartile range (IQR)	431	439

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	252.47345	257.66194
Coefficient of variation (CV)	0.58307957	0.57723275
Kurtosis	-1.1647224	-1.194053
Mean	433	446.37444
Median Absolute Deviation (MAD)	214.5	221
Skewness	0.060243897	0.049195146
Sum	193118	199083
Variance	63742.845	66389.677
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
401	1	0.2%
561	1	0.2%
770	1	0.2%
373	1	0.2%
307	1	0.2%
76	1	0.2%
4	1	0.2%
791	1	0.2%
211	1	0.2%
845	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
56	1	0.2%
37	1	0.2%
767	1	0.2%
525	1	0.2%
684	1	0.2%
875	1	0.2%
723	1	0.2%
470	1	0.2%
189	1	0.2%
444	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
1	1	0.2%
4	1	0.2%
5	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
17	1	0.2%
18	1	0.2%

Value	Count	Frequency (%)
3	1	0.2%
4	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
11	1	0.2%
12	1	0.2%
13	1	0.2%
15	1	0.2%
17	1	0.2%

Value	Count	Frequency (%)
3	1	0.2%
4	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
11	1	0.2%
12	1	0.2%
13	1	0.2%
15	1	0.2%
17	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
4	1	0.2%
5	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
17	1	0.2%
18	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	278
1	168

0	278
1	168

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	1
2nd row	0	0
3rd row	0	0
4th row	1	0
5th row	0	1

Common Values

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring characters

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	237
1	108
2	101

3	246
1	114
2	86

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	3
2nd row	3	1
3rd row	3	3
4th row	1	3
5th row	3	2

Common Values

Value	Count	Frequency (%)
3	237	53.1%
1	108	24.2%
2	101	22.6%

Value	Count	Frequency (%)
3	246	55.2%
1	114	25.6%
2	86	19.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	237	53.1%
1	108	24.2%
2	101	22.6%

Value	Count	Frequency (%)
3	246	55.2%
1	114	25.6%
2	86	19.3%

Most occurring characters

Value	Count	Frequency (%)
3	237	53.1%
1	108	24.2%
2	101	22.6%

Value	Count	Frequency (%)
3	246	55.2%
1	114	25.6%
2	86	19.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	237	53.1%
1	108	24.2%
2	101	22.6%

Value	Count	Frequency (%)
3	246	55.2%
1	114	25.6%
2	86	19.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	237	53.1%
1	108	24.2%
2	101	22.6%

Value	Count	Frequency (%)
3	246	55.2%
1	114	25.6%
2	86	19.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	237	53.1%
1	108	24.2%
2	101	22.6%

Value	Count	Frequency (%)
3	246	55.2%
1	114	25.6%
2	86	19.3%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	61
Median length	50	46
Mean length	26.742152	26.596413
Min length	13	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	11927	11862
Distinct characters	60	60
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Morrow, Mr. Thomas Rowan	Mamee, Mr. Hanna
2nd row	Gronnestad, Mr. Daniel Danielsen	Brewe, Dr. Arthur Jackson
3rd row	Beavan, Mr. William Thomas	Kassem, Mr. Fared
4th row	Fleming, Miss. Margaret	Goodwin, Mr. Charles Edward
5th row	Moen, Mr. Sigurd Hansen	Abelson, Mrs. Samuel (Hannah Wizosky)

Value	Count	Frequency (%)
mr	268	14.8%
miss	96	5.3%
mrs	62	3.4%
william	34	1.9%
john	18	1.0%
henry	17	0.9%
james	16	0.9%
master	16	0.9%
george	14	0.8%
charles	12	0.7%
Other values (872)	1255	69.4%

Value	Count	Frequency (%)
mr	269	15.0%
miss	95	5.3%
mrs	54	3.0%
william	36	2.0%
henry	18	1.0%
john	17	0.9%
master	17	0.9%
george	16	0.9%
james	13	0.7%
charles	13	0.7%
Other values (899)	1244	69.4%

Most occurring characters

Value	Count	Frequency (%)
	1363	11.4%
r	971	8.1%
e	877	7.4%
a	823	6.9%
i	673	5.6%
s	630	5.3%
n	611	5.1%
M	565	4.7%
l	543	4.6%
o	462	3.9%
Other values (50)	4409	37.0%

Value	Count	Frequency (%)
	1346	11.3%
r	949	8.0%
e	851	7.2%
a	788	6.6%
i	678	5.7%
s	659	5.6%
n	632	5.3%
M	541	4.6%
l	532	4.5%
o	508	4.3%
Other values (50)	4378	36.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11927	100.0%

Value	Count	Frequency (%)
(unknown)	11862	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1363	11.4%
r	971	8.1%
e	877	7.4%
a	823	6.9%
i	673	5.6%
s	630	5.3%
n	611	5.1%
M	565	4.7%
l	543	4.6%
o	462	3.9%
Other values (50)	4409	37.0%

Value	Count	Frequency (%)
	1346	11.3%
r	949	8.0%
e	851	7.2%
a	788	6.6%
i	678	5.7%
s	659	5.6%
n	632	5.3%
M	541	4.6%
l	532	4.5%
o	508	4.3%
Other values (50)	4378	36.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11927	100.0%

Value	Count	Frequency (%)
(unknown)	11862	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1363	11.4%
r	971	8.1%
e	877	7.4%
a	823	6.9%
i	673	5.6%
s	630	5.3%
n	611	5.1%
M	565	4.7%
l	543	4.6%
o	462	3.9%
Other values (50)	4409	37.0%

Value	Count	Frequency (%)
	1346	11.3%
r	949	8.0%
e	851	7.2%
a	788	6.6%
i	678	5.7%
s	659	5.6%
n	632	5.3%
M	541	4.6%
l	532	4.5%
o	508	4.3%
Other values (50)	4378	36.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11927	100.0%

Value	Count	Frequency (%)
(unknown)	11862	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1363	11.4%
r	971	8.1%
e	877	7.4%
a	823	6.9%
i	673	5.6%
s	630	5.3%
n	611	5.1%
M	565	4.7%
l	543	4.6%
o	462	3.9%
Other values (50)	4409	37.0%

Value	Count	Frequency (%)
	1346	11.3%
r	949	8.0%
e	851	7.2%
a	788	6.6%
i	678	5.7%
s	659	5.6%
n	632	5.3%
M	541	4.6%
l	532	4.5%
o	508	4.3%
Other values (50)	4378	36.9%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	288
female	158

male	294
female	152

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7085202	4.6816143
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2100	2088
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	male
3rd row	male	male
4th row	female	male
5th row	male	female

Common Values

Value	Count	Frequency (%)
male	288	64.6%
female	158	35.4%

Value	Count	Frequency (%)
male	294	65.9%
female	152	34.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	288	64.6%
female	158	35.4%

Value	Count	Frequency (%)
male	294	65.9%
female	152	34.1%

Most occurring characters

Value	Count	Frequency (%)
e	604	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	158	7.5%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2100	100.0%

Value	Count	Frequency (%)
(unknown)	2088	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	604	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	158	7.5%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2100	100.0%

Value	Count	Frequency (%)
(unknown)	2088	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	604	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	158	7.5%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2100	100.0%

Value	Count	Frequency (%)
(unknown)	2088	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	604	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	158	7.5%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	74	75
Distinct (%)	20.7%	20.7%
Missing	88	83
Missing (%)	19.7%	18.6%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.750698	29.955923

	Dataset A	Dataset B
Minimum	0.42	0.67
Maximum	80	80
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.67
5-th percentile	4.85	4
Q1	21	21
median	29	28
Q3	38	38
95-th percentile	54	57.8
Maximum	80	80
Range	79.58	79.33
Interquartile range (IQR)	17	17

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.074854	14.624023
Coefficient of variation (CV)	0.47309323	0.48818468
Kurtosis	0.11118063	0.42182478
Mean	29.750698	29.955923
Median Absolute Deviation (MAD)	8.25	8
Skewness	0.27959062	0.46640687
Sum	10650.75	10874
Variance	198.10151	213.86204
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
24	16	3.6%
22	15	3.4%
36	14	3.1%
18	14	3.1%
30	13	2.9%
35	13	2.9%
21	12	2.7%
31	12	2.7%
28	11	2.5%
34	10	2.2%
Other values (64)	228	51.1%
(Missing)	88	19.7%

Value	Count	Frequency (%)
24	20	4.5%
18	15	3.4%
28	13	2.9%
36	13	2.9%
30	12	2.7%
25	12	2.7%
26	11	2.5%
20	11	2.5%
21	11	2.5%
40	10	2.2%
Other values (65)	235	52.7%
(Missing)	83	18.6%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	2	0.4%
2	4	0.9%
3	3	0.7%
4	5	1.1%
5	2	0.4%
6	3	0.7%
7	2	0.4%

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	2	0.4%
2	7	1.6%
3	2	0.4%
4	6	1.3%
6	3	0.7%
7	1	0.2%
8	1	0.2%

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	2	0.4%
2	7	1.6%
3	2	0.4%
4	6	1.3%
6	3	0.7%
7	1	0.2%
8	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	2	0.4%
2	4	0.9%
3	3	0.7%
4	5	1.1%
5	2	0.4%
6	3	0.7%
7	2	0.4%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.51345291	0.53363229

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	311	308
Zeros (%)	69.7%	69.1%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	2.75
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.1469738	1.1775002
Coefficient of variation (CV)	2.2338442	2.206576
Kurtosis	20.43215	18.648508
Mean	0.51345291	0.53363229
Median Absolute Deviation (MAD)	0	0
Skewness	4.0105825	3.8637579
Sum	229	238
Variance	1.3155489	1.3865068
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	311	69.7%
1	97	21.7%
2	17	3.8%
3	8	1.8%
4	6	1.3%
8	5	1.1%
5	2	0.4%

Value	Count	Frequency (%)
0	308	69.1%
1	99	22.2%
2	16	3.6%
3	9	2.0%
8	5	1.1%
4	5	1.1%
5	4	0.9%

Value	Count	Frequency (%)
0	311	69.7%
1	97	21.7%
2	17	3.8%
3	8	1.8%
4	6	1.3%
5	2	0.4%
8	5	1.1%

Value	Count	Frequency (%)
0	308	69.1%
1	99	22.2%
2	16	3.6%
3	9	2.0%
4	5	1.1%
5	4	0.9%
8	5	1.1%

Value	Count	Frequency (%)
0	308	69.1%
1	99	22.2%
2	16	3.6%
3	9	2.0%
4	5	1.1%
5	4	0.9%
8	5	1.1%

Value	Count	Frequency (%)
0	311	69.7%
1	97	21.7%
2	17	3.8%
3	8	1.8%
4	6	1.3%
5	2	0.4%
8	5	1.1%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	6
Distinct (%)	1.6%	1.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.37443946	0.35201794

	Dataset A	Dataset B
Minimum	0	0
Maximum	6	5
Zeros	342	345
Zeros (%)	76.7%	77.4%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	6	5
Range	6	5
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.80513606	0.74612585
Coefficient of variation (CV)	2.1502436	2.1195677
Kurtosis	10.897371	8.1967426
Mean	0.37443946	0.35201794
Median Absolute Deviation (MAD)	0	0
Skewness	2.8525129	2.5537868
Sum	167	157
Variance	0.64824407	0.55670378
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	342	76.7%
1	57	12.8%
2	39	8.7%
3	4	0.9%
5	2	0.4%
6	1	0.2%
4	1	0.2%

Value	Count	Frequency (%)
0	345	77.4%
1	55	12.3%
2	41	9.2%
5	2	0.4%
3	2	0.4%
4	1	0.2%

Value	Count	Frequency (%)
0	342	76.7%
1	57	12.8%
2	39	8.7%
3	4	0.9%
4	1	0.2%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	345	77.4%
1	55	12.3%
2	41	9.2%
3	2	0.4%
4	1	0.2%
5	2	0.4%

Value	Count	Frequency (%)
0	345	77.4%
1	55	12.3%
2	41	9.2%
3	2	0.4%
4	1	0.2%
5	2	0.4%

Value	Count	Frequency (%)
0	342	76.7%
1	57	12.8%
2	39	8.7%
3	4	0.9%
4	1	0.2%
5	2	0.4%
6	1	0.2%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	382	389
Distinct (%)	85.7%	87.2%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	7.0538117	6.6793722
Min length	3	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	3146	2979
Distinct characters	32	27
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	331	344 ?
Unique (%)	74.2%	77.1%

Sample

	Dataset A	Dataset B
1st row	372622	2677
2nd row	8471	112379
3rd row	323951	2700
4th row	17421	CA 2144
5th row	348123	P/PP 3381

Value	Count	Frequency (%)
pc	30	5.1%
c.a	16	2.7%
a/5	11	1.9%
ca	8	1.4%
soton/o.q	8	1.4%
2	7	1.2%
ston/o	7	1.2%
2343	5	0.9%
soton/oq	5	0.9%
w./c	4	0.7%
Other values (403)	482	82.7%

Value	Count	Frequency (%)
pc	32	5.7%
c.a	11	1.9%
ca	10	1.8%
a/5	9	1.6%
soton/o.q	5	0.9%
w./c	5	0.9%
2343	5	0.9%
ston/o	4	0.7%
2	4	0.7%
a/4	4	0.7%
Other values (408)	476	84.2%

Most occurring characters

Value	Count	Frequency (%)
3	384	12.2%
1	348	11.1%
2	304	9.7%
7	235	7.5%
6	211	6.7%
0	210	6.7%
4	210	6.7%
5	203	6.5%
9	159	5.1%
8	145	4.6%
Other values (22)	737	23.4%

Value	Count	Frequency (%)
3	359	12.1%
1	354	11.9%
2	271	9.1%
7	255	8.6%
4	245	8.2%
0	215	7.2%
6	202	6.8%
5	193	6.5%
9	158	5.3%
8	142	4.8%
Other values (17)	585	19.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3146	100.0%

Value	Count	Frequency (%)
(unknown)	2979	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	384	12.2%
1	348	11.1%
2	304	9.7%
7	235	7.5%
6	211	6.7%
0	210	6.7%
4	210	6.7%
5	203	6.5%
9	159	5.1%
8	145	4.6%
Other values (22)	737	23.4%

Value	Count	Frequency (%)
3	359	12.1%
1	354	11.9%
2	271	9.1%
7	255	8.6%
4	245	8.2%
0	215	7.2%
6	202	6.8%
5	193	6.5%
9	158	5.3%
8	142	4.8%
Other values (17)	585	19.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3146	100.0%

Value	Count	Frequency (%)
(unknown)	2979	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	384	12.2%
1	348	11.1%
2	304	9.7%
7	235	7.5%
6	211	6.7%
0	210	6.7%
4	210	6.7%
5	203	6.5%
9	159	5.1%
8	145	4.6%
Other values (22)	737	23.4%

Value	Count	Frequency (%)
3	359	12.1%
1	354	11.9%
2	271	9.1%
7	255	8.6%
4	245	8.2%
0	215	7.2%
6	202	6.8%
5	193	6.5%
9	158	5.3%
8	142	4.8%
Other values (17)	585	19.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3146	100.0%

Value	Count	Frequency (%)
(unknown)	2979	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	384	12.2%
1	348	11.1%
2	304	9.7%
7	235	7.5%
6	211	6.7%
0	210	6.7%
4	210	6.7%
5	203	6.5%
9	159	5.1%
8	145	4.6%
Other values (22)	737	23.4%

Value	Count	Frequency (%)
3	359	12.1%
1	354	11.9%
2	271	9.1%
7	255	8.6%
4	245	8.2%
0	215	7.2%
6	202	6.8%
5	193	6.5%
9	158	5.3%
8	142	4.8%
Other values (17)	585	19.6%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	170	173
Distinct (%)	38.1%	38.8%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.869544	34.032454

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	11	9
Zeros (%)	2.5%	2.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.05	7.162525
Q1	7.925	7.8958
median	14.4542	14.4542
Q3	29.7	31.275
95-th percentile	108.28125	130.2375
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	21.775	23.3792

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	44.323541	54.25907
Coefficient of variation (CV)	1.4839042	1.5943331
Kurtosis	37.58623	30.286601
Mean	29.869544	34.032454
Median Absolute Deviation (MAD)	7.2042	7.2042
Skewness	4.9118206	4.6439131
Sum	13321.817	15178.475
Variance	1964.5763	2944.0467
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8.05	25	5.6%
13	23	5.2%
26	18	4.0%
7.75	16	3.6%
10.5	12	2.7%
0	11	2.5%
7.925	10	2.2%
7.8958	9	2.0%
7.8542	8	1.8%
8.6625	8	1.8%
Other values (160)	306	68.6%

Value	Count	Frequency (%)
13	23	5.2%
8.05	21	4.7%
7.75	18	4.0%
26	18	4.0%
7.8958	17	3.8%
10.5	11	2.5%
26.55	10	2.2%
8.6625	10	2.2%
0	9	2.0%
7.775	9	2.0%
Other values (163)	300	67.3%

Value	Count	Frequency (%)
0	11	2.5%
6.4958	2	0.4%
6.8583	1	0.2%
6.95	1	0.2%
6.975	2	0.4%
7.0458	1	0.2%
7.05	6	1.3%
7.0542	2	0.4%
7.125	4	0.9%
7.1417	1	0.2%

Value	Count	Frequency (%)
0	9	2.0%
6.4375	1	0.2%
6.4958	2	0.4%
6.75	2	0.4%
6.975	1	0.2%
7.05	5	1.1%
7.0542	1	0.2%
7.125	1	0.2%
7.1417	1	0.2%
7.225	8	1.8%

Value	Count	Frequency (%)
0	9	2.0%
6.4375	1	0.2%
6.4958	2	0.4%
6.75	2	0.4%
6.975	1	0.2%
7.05	5	1.1%
7.0542	1	0.2%
7.125	1	0.2%
7.1417	1	0.2%
7.225	8	1.8%

Value	Count	Frequency (%)
0	11	2.5%
6.4958	2	0.4%
6.8583	1	0.2%
6.95	1	0.2%
6.975	2	0.4%
7.0458	1	0.2%
7.05	6	1.3%
7.0542	2	0.4%
7.125	4	0.9%
7.1417	1	0.2%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	88	95
Distinct (%)	81.5%	88.0%
Missing	338	338
Missing (%)	75.8%	75.8%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	11	15
Median length	3	3
Mean length	3.5092593	3.6111111
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	379	390
Distinct characters	19	19
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	69	85 ?
Unique (%)	63.9%	78.7%

Sample

	Dataset A	Dataset B
1st row	F G73	E49
2nd row	C123	C103
3rd row	F G73	B94
4th row	E63	C123
5th row	D6	F G73

Value	Count	Frequency (%)
b96	3	2.4%
b98	3	2.4%
f	3	2.4%
g73	2	1.6%
c22	2	1.6%
c26	2	1.6%
c123	2	1.6%
b58	2	1.6%
b60	2	1.6%
e24	2	1.6%
Other values (85)	100	81.3%

Value	Count	Frequency (%)
g6	3	2.4%
c23	3	2.4%
c25	3	2.4%
c27	3	2.4%
b96	3	2.4%
b98	3	2.4%
e67	2	1.6%
f2	2	1.6%
e8	2	1.6%
c123	2	1.6%
Other values (94)	101	79.5%

Most occurring characters

Value	Count	Frequency (%)
2	40	10.6%
C	37	9.8%
1	37	9.8%
B	31	8.2%
6	27	7.1%
3	27	7.1%
E	21	5.5%
8	21	5.5%
9	19	5.0%
7	18	4.7%
Other values (9)	101	26.6%

Value	Count	Frequency (%)
C	41	10.5%
2	38	9.7%
B	35	9.0%
3	32	8.2%
6	30	7.7%
1	29	7.4%
9	22	5.6%
8	21	5.4%
5	20	5.1%
7	19	4.9%
Other values (9)	103	26.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	379	100.0%

Value	Count	Frequency (%)
(unknown)	390	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	40	10.6%
C	37	9.8%
1	37	9.8%
B	31	8.2%
6	27	7.1%
3	27	7.1%
E	21	5.5%
8	21	5.5%
9	19	5.0%
7	18	4.7%
Other values (9)	101	26.6%

Value	Count	Frequency (%)
C	41	10.5%
2	38	9.7%
B	35	9.0%
3	32	8.2%
6	30	7.7%
1	29	7.4%
9	22	5.6%
8	21	5.4%
5	20	5.1%
7	19	4.9%
Other values (9)	103	26.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	379	100.0%

Value	Count	Frequency (%)
(unknown)	390	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	40	10.6%
C	37	9.8%
1	37	9.8%
B	31	8.2%
6	27	7.1%
3	27	7.1%
E	21	5.5%
8	21	5.5%
9	19	5.0%
7	18	4.7%
Other values (9)	101	26.6%

Value	Count	Frequency (%)
C	41	10.5%
2	38	9.7%
B	35	9.0%
3	32	8.2%
6	30	7.7%
1	29	7.4%
9	22	5.6%
8	21	5.4%
5	20	5.1%
7	19	4.9%
Other values (9)	103	26.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	379	100.0%

Value	Count	Frequency (%)
(unknown)	390	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	40	10.6%
C	37	9.8%
1	37	9.8%
B	31	8.2%
6	27	7.1%
3	27	7.1%
E	21	5.5%
8	21	5.5%
9	19	5.0%
7	18	4.7%
Other values (9)	101	26.6%

Value	Count	Frequency (%)
C	41	10.5%
2	38	9.7%
B	35	9.0%
3	32	8.2%
6	30	7.7%
1	29	7.4%
9	22	5.6%
8	21	5.4%
5	20	5.1%
7	19	4.9%
Other values (9)	103	26.4%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	1
Missing (%)	0.2%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	333
C	73
Q	39

S	332
C	83
Q	30

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	Q	C
2nd row	S	C
3rd row	S	C
4th row	C	S
5th row	S	C

Common Values

Value	Count	Frequency (%)
S	333	74.7%
C	73	16.4%
Q	39	8.7%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	332	74.4%
C	83	18.6%
Q	30	6.7%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	333	74.8%
c	73	16.4%
q	39	8.8%

Value	Count	Frequency (%)
s	332	74.6%
c	83	18.7%
q	30	6.7%

Most occurring characters

Value	Count	Frequency (%)
S	333	74.8%
C	73	16.4%
Q	39	8.8%

Value	Count	Frequency (%)
S	332	74.6%
C	83	18.7%
Q	30	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	333	74.8%
C	73	16.4%
Q	39	8.8%

Value	Count	Frequency (%)
S	332	74.6%
C	83	18.7%
Q	30	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	333	74.8%
C	73	16.4%
Q	39	8.8%

Value	Count	Frequency (%)
S	332	74.6%
C	83	18.7%
Q	30	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	333	74.8%
C	73	16.4%
Q	39	8.8%

Value	Count	Frequency (%)
S	332	74.6%
C	83	18.7%
Q	30	6.7%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.109	0.160	-0.232	-0.001	0.269	0.149	-0.192	0.239
Embarked	0.109	1.000	0.197	0.000	0.000	0.244	0.123	0.138	0.177
Fare	0.160	0.197	1.000	0.406	-0.053	0.449	0.146	0.410	0.167
Parch	-0.232	0.000	0.406	1.000	0.021	0.000	0.300	0.439	0.151
PassengerId	-0.001	0.000	-0.053	0.021	1.000	0.000	0.050	-0.065	0.071
Pclass	0.269	0.244	0.449	0.000	0.000	1.000	0.036	0.113	0.317
Sex	0.149	0.123	0.146	0.300	0.050	0.036	1.000	0.226	0.492
SibSp	-0.192	0.138	0.410	0.439	-0.065	0.113	0.226	1.000	0.138
Survived	0.239	0.177	0.167	0.151	0.071	0.317	0.492	0.138	1.000

Dataset B

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.000	0.183	-0.258	-0.054	0.307	0.161	-0.123	0.215
Embarked	0.000	1.000	0.209	0.000	0.000	0.257	0.083	0.000	0.152
Fare	0.183	0.209	1.000	0.424	-0.053	0.480	0.187	0.466	0.254
Parch	-0.258	0.000	0.424	1.000	0.035	0.000	0.287	0.472	0.182
PassengerId	-0.054	0.000	-0.053	0.035	1.000	0.000	0.076	-0.000	0.065
Pclass	0.307	0.257	0.480	0.000	0.000	1.000	0.114	0.163	0.303
Sex	0.161	0.083	0.187	0.287	0.076	0.114	1.000	0.164	0.538
SibSp	-0.123	0.000	0.466	0.472	-0.000	0.163	0.164	1.000	0.203
Survived	0.215	0.152	0.254	0.182	0.065	0.303	0.538	0.203	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
560	561	0	3	Morrow, Mr. Thomas Rowan	male	NaN	0	0	372622	7.7500	NaN	Q
769	770	0	3	Gronnestad, Mr. Daniel Danielsen	male	32.0	0	0	8471	8.3625	NaN	S
372	373	0	3	Beavan, Mr. William Thomas	male	19.0	0	0	323951	8.0500	NaN	S
306	307	1	1	Fleming, Miss. Margaret	female	NaN	0	0	17421	110.8833	NaN	C
75	76	0	3	Moen, Mr. Sigurd Hansen	male	25.0	0	0	348123	7.6500	F G73	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
790	791	0	3	Keane, Mr. Andrew "Andy"	male	NaN	0	0	12460	7.7500	NaN	Q
210	211	0	3	Ali, Mr. Ahmed	male	24.0	0	0	SOTON/O.Q. 3101311	7.0500	NaN	S
844	845	0	3	Culumovic, Mr. Jeso	male	17.0	0	0	315090	8.6625	NaN	S
348	349	1	3	Coutts, Master. William Loch "William"	male	3.0	1	1	C.A. 37671	15.9000	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
36	37	1	3	Mamee, Mr. Hanna	male	NaN	0	0	2677	7.2292	NaN	C
766	767	0	1	Brewe, Dr. Arthur Jackson	male	NaN	0	0	112379	39.6000	NaN	C
524	525	0	3	Kassem, Mr. Fared	male	NaN	0	0	2700	7.2292	NaN	C
683	684	0	3	Goodwin, Mr. Charles Edward	male	14.00	5	2	CA 2144	46.9000	NaN	S
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.00	1	0	P/PP 3381	24.0000	NaN	C
722	723	0	2	Gillespie, Mr. William Henry	male	34.00	0	0	12233	13.0000	NaN	S
469	470	1	3	Baclini, Miss. Helene Barbara	female	0.75	2	1	2666	19.2583	NaN	C
188	189	0	3	Bourke, Mr. John	male	40.00	1	1	364849	15.5000	NaN	Q
443	444	1	2	Reynaldo, Ms. Encarnacion	female	28.00	0	0	230434	13.0000	NaN	S
535	536	1	2	Hart, Miss. Eva Miriam	female	7.00	0	2	F.C.C. 13529	26.2500	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
277	278	0	2	Parkes, Mr. Francis "Frank"	male	NaN	0	0	239853	0.0000	NaN	S
478	479	0	3	Karlsson, Mr. Nils August	male	22.0	0	0	350060	7.5208	NaN	S
784	785	0	3	Ali, Mr. William	male	25.0	0	0	SOTON/O.Q. 3101312	7.0500	NaN	S
465	466	0	3	Goncalves, Mr. Manuel Estanslas	male	38.0	0	0	SOTON/O.Q. 3101306	7.0500	NaN	S
806	807	0	1	Andrews, Mr. Thomas Jr	male	39.0	0	0	112050	0.0000	A36	S
802	803	1	1	Carter, Master. William Thornton II	male	11.0	1	2	113760	120.0000	B96 B98	S
880	881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
702	703	0	3	Barbara, Miss. Saiide	female	18.0	0	1	2691	14.4542	NaN	C
169	170	0	3	Ling, Mr. Lee	male	28.0	0	0	1601	56.4958	NaN	S
400	401	1	3	Niskanen, Mr. Juha	male	39.0	0	0	STON/O 2. 3101289	7.9250	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
855	856	1	3	Aks, Mrs. Sam (Leah Rosen)	female	18.0	0	1	392091	9.3500	NaN	S
565	566	0	3	Davies, Mr. Alfred J	male	24.0	2	0	A/4 48871	24.1500	NaN	S
276	277	0	3	Lindblom, Miss. Augusta Charlotta	female	45.0	0	0	347073	7.7500	NaN	S
131	132	0	3	Coelho, Mr. Domingos Fernandeo	male	20.0	0	0	SOTON/O.Q. 3101307	7.0500	NaN	S
780	781	1	3	Ayoub, Miss. Banoura	female	13.0	0	0	2687	7.2292	NaN	C
376	377	1	3	Landergren, Miss. Aurora Adelia	female	22.0	0	0	C 7077	7.2500	NaN	S
666	667	0	2	Butler, Mr. Reginald Fenton	male	25.0	0	0	234686	13.0000	NaN	S
355	356	0	3	Vanden Steen, Mr. Leo Peter	male	28.0	0	0	345783	9.5000	NaN	S
394	395	1	3	Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)	female	24.0	0	2	PP 9549	16.7000	G6	S
55	56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5000	C52	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)